The story


Scarcity in the workforce isn’t new for Southwest Minnesota, but it’s severity is increasing. Demographic changes and economic growth are all contributing to the problem.

Due to this issue, leaders are looking to invest in programs and initiatives that will help grow the workforce. One of the primary areas is education.

It’s well recorded that since the 1970s, education has played a dominant role in training our workforce to meet the economic demands. However, there is also a lot of evidence showing that education has encouraged young people to leave rural areas to find greater economic opportunity. Leaders are now looking at education as a tool to grow their local, rural workforce and it’s role in promoting local opportunities.


Meaningful employment at each Time X

So, lets answer the main question, what percentage of graduates from Southwest Minnesota high schools have meaningful employment the year they graduate, two years after they graduate, and seven years after they graduate.

The chart below provides the percentage of graduates in Southwest Minnesota from 2008 to 2019 that had meaningful employment in the same county or EDR as their high school, within one of the 3 research EDRs, or in Minnesota. There are two trends to notice. First, is that the percentage increases as the number of years after graduating high school increases. Second, the percentages increase as the geographic location of measuring meaningful employment expands. This culminates when looking at the percentage of individuals from a Southwest Minnesota high school had meaningful employment in Minnesota seven years after graduation - 56.6% of them.



What economic development leaders across Southwest Minnesota want to know is what factors play a role in people having meaningful employment in their local geography.

The factors at Time X = grad year +0

The Classification and Regression Tree analysis concludes the following variables in our dataset as being important in predicting whether an individual has meaningful employment in the same regions as their high school the year of graduating high school.

  1. High school accomplishments
    1. took.act
    2. cte.achievement
  2. High school enrollment
    1. pseo.participant
  3. Post-secondary pathway
    1. attended.ps.within.first.year.hsgrad
  4. High school characteristics
    1. Dem_Desc
  5. Demographic variables
    1. Race/Ethnicity

The charts below show the following;

A statistically significantly larger proportion of individuals with the following attributes had meaningful employment in the same geography as their high school;

  • Did NOT take the ACT
  • Took CTE courses (especially completors and concentrators)
  • Did NOT take PSEO courses
  • Did NOT attend post-secondary within the first year of graduating high school
  • Graduated from a town/rural mix high school
  • Were hispanic or black


Factors - high school accomplishments

Took ACT

A significantly larger proportion of individuals that did not take the ACT in high school had meaningful employment in the same county and EDR as their high school, or had meaningful employment in one of the three EDRS and/or Minnesota.


Achievement in Career and Technical Education

A significantly larger proportion of individual that were categorized as completors or concentrators in CTE programming had meaningful employment in the same county or EDR as their high school, or in one of the three EDRs and/or Minnesota. This was also true for individuals that participated in CTE courses but were not concentrators or completors.


Took ACT



County employment match
emp.match took.ACT Total
No Yes
Employment 2197
1565
17.4 %
2523
3155
9.9 %
4720
4720
12.4 %
No employment 10416
11048
82.6 %
22914
22282
90.1 %
33330
33330
87.6 %
Total 12613
12613
100 %
25437
25437
100 %
38050
38050
100 %
χ2=435.800 · df=1 · φ=0.107 · p=0.000
EDR employment match
emp.match took.ACT Total
No Yes
Employment 2650
1873
21 %
3001
3778
11.8 %
5651
5651
14.9 %
No employment 9963
10740
79 %
22436
21659
88.2 %
32399
32399
85.1 %
Total 12613
12613
100 %
25437
25437
100 %
38050
38050
100 %
χ2=565.143 · df=1 · φ=0.122 · p=0.000


Any three EDR employment match
emp.match took.ACT Total
No Yes
Employment 2917
2043
23.1 %
3246
4120
12.8 %
6163
6163
16.2 %
No employment 9696
10570
76.9 %
22191
21317
87.2 %
31887
31887
83.8 %
Total 12613
12613
100 %
25437
25437
100 %
38050
38050
100 %
χ2=666.742 · df=1 · φ=0.132 · p=0.000
MN employment match
emp.match took.ACT Total
No Yes
Employment 3505
2466
27.8 %
3934
4973
15.5 %
7439
7439
19.6 %
No employment 9108
10147
72.2 %
21503
20464
84.5 %
30611
30611
80.4 %
Total 12613
12613
100 %
25437
25437
100 %
38050
38050
100 %
χ2=813.338 · df=1 · φ=0.146 · p=0.000


Achievement in career and technology pathways



County employment match
emp.match cte.achievement Total
CTE Concentrator or
Completor
CTE Participant No CTE
Employment 2630
2212
14.8 %
1304
1388
11.7 %
786
1120
8.7 %
4720
4720
12.4 %
No employment 15198
15616
85.2 %
9888
9804
88.3 %
8244
7910
91.3 %
33330
33330
87.6 %
Total 17828
17828
100 %
11192
11192
100 %
9030
9030
100 %
38050
38050
100 %
χ2=210.047 · df=2 · Cramer’s V=0.074 · p=0.000
EDR employment match
emp.match cte.achievement Total
CTE Concentrator or
Completor
CTE Participant No CTE
Employment 3156
2648
17.7 %
1545
1662
13.8 %
950
1341
10.5 %
5651
5651
14.9 %
No employment 14672
15180
82.3 %
9647
9530
86.2 %
8080
7689
89.5 %
32399
32399
85.1 %
Total 17828
17828
100 %
11192
11192
100 %
9030
9030
100 %
38050
38050
100 %
χ2=258.234 · df=2 · Cramer’s V=0.082 · p=0.000


Any three EDR employment match
emp.match cte.achievement Total
CTE Concentrator or
Completor
CTE Participant No CTE
Employment 3432
2888
19.3 %
1682
1813
15 %
1049
1463
11.6 %
6163
6163
16.2 %
No employment 14396
14940
80.7 %
9510
9379
85 %
7981
7567
88.4 %
31887
31887
83.8 %
Total 17828
17828
100 %
11192
11192
100 %
9030
9030
100 %
38050
38050
100 %
χ2=273.286 · df=2 · Cramer’s V=0.085 · p=0.000
MN employment match
emp.match cte.achievement Total
CTE Concentrator or
Completor
CTE Participant No CTE
Employment 4123
3485
23.1 %
2019
2188
18 %
1297
1765
14.4 %
7439
7439
19.6 %
No employment 13705
14343
76.9 %
9173
9004
82 %
7733
7265
85.6 %
30611
30611
80.4 %
Total 17828
17828
100 %
11192
11192
100 %
9030
9030
100 %
38050
38050
100 %
χ2=315.678 · df=2 · Cramer’s V=0.091 · p=0.000


Factors - high school enrollment

Enrolled in PSEO

A significantly larger proportion of individuals that never enrolled in PSEO courses had meaningful employment the year they graduated high school.The percentage also increased for these individuals as the geography enlarged.


Enrolled in PSEO



County employment match
emp.match pseo.participant Total
No Yes
Employment 3608
3081
14.5 %
1112
1639
8.4 %
4720
4720
12.4 %
No employment 21233
21760
85.5 %
12097
11570
91.6 %
33330
33330
87.6 %
Total 24841
24841
100 %
13209
13209
100 %
38050
38050
100 %
χ2=295.316 · df=1 · φ=0.088 · p=0.000
EDR employment match
emp.match pseo.participant Total
No Yes
Employment 4332
3689
17.4 %
1319
1962
10 %
5651
5651
14.9 %
No employment 20509
21152
82.6 %
11890
11247
90 %
32399
32399
85.1 %
Total 24841
24841
100 %
13209
13209
100 %
38050
38050
100 %
χ2=378.231 · df=1 · φ=0.100 · p=0.000


Any three EDR employment match
emp.match pseo.participant Total
No Yes
Employment 4743
4024
19.1 %
1420
2139
10.8 %
6163
6163
16.2 %
No employment 20098
20817
80.9 %
11789
11070
89.2 %
31887
31887
83.8 %
Total 24841
24841
100 %
13209
13209
100 %
38050
38050
100 %
χ2=441.619 · df=1 · φ=0.108 · p=0.000
MN employment match
emp.match pseo.participant Total
No Yes
Employment 5700
4857
22.9 %
1739
2582
13.2 %
7439
7439
19.6 %
No employment 19141
19984
77.1 %
11470
10627
86.8 %
30611
30611
80.4 %
Total 24841
24841
100 %
13209
13209
100 %
38050
38050
100 %
χ2=523.870 · df=1 · φ=0.117 · p=0.000


Factors - post-secondary pathway

Attended post-secondary within first year after graduating high school

A significantly larger proportion of individuals that did not enroll in post-secondary within the first year of high school had meaningful employment the year they graduated high school.The percentage also increased for these individuals as the geography enlarged.


Attended post-secondary within first year after graduating high school



County employment match
emp.match attended.ps.within.first.year.hsgrad Total
No Yes
Employment 1803
1316
17 %
2917
3404
10.6 %
4720
4720
12.4 %
No employment 8802
9289
83 %
24528
24041
89.4 %
33330
33330
87.6 %
Total 10605
10605
100 %
27445
27445
100 %
38050
38050
100 %
χ2=285.320 · df=1 · φ=0.087 · p=0.000
EDR employment match
emp.match attended.ps.within.first.year.hsgrad Total
No Yes
Employment 2212
1575
20.9 %
3439
4076
12.5 %
5651
5651
14.9 %
No employment 8393
9030
79.1 %
24006
23369
87.5 %
32399
32399
85.1 %
Total 10605
10605
100 %
27445
27445
100 %
38050
38050
100 %
χ2=418.819 · df=1 · φ=0.105 · p=0.000


Any three EDR employment match
emp.match attended.ps.within.first.year.hsgrad Total
No Yes
Employment 2462
1718
23.2 %
3701
4445
13.5 %
6163
6163
16.2 %
No employment 8143
8887
76.8 %
23744
23000
86.5 %
31887
31887
83.8 %
Total 10605
10605
100 %
27445
27445
100 %
38050
38050
100 %
χ2=532.835 · df=1 · φ=0.118 · p=0.000
MN employment match
emp.match attended.ps.within.first.year.hsgrad Total
No Yes
Employment 2985
2073
28.1 %
4454
5366
16.2 %
7439
7439
19.6 %
No employment 7620
8532
71.9 %
22991
22079
83.8 %
30611
30611
80.4 %
Total 10605
10605
100 %
27445
27445
100 %
38050
38050
100 %
χ2=690.060 · df=1 · φ=0.135 · p=0.000


Factors - high school characteristics

High school county RUCA category

A significantly larger proportion of individuals that did not enroll in post-secondary within the first year of high school had meaningful employment the year they graduated high school.The percentage also increased for these individuals as the geography enlarged.


High school county RUCA category



County employment match
emp.match Dem_Desc Total
Entirely rural Town/rural mix Urban/town/rural mix
Employment 476
684
8.6 %
3575
3294
13.5 %
669
742
11.2 %
4720
4720
12.4 %
No employment 5039
4831
91.4 %
22979
23260
86.5 %
5312
5239
88.8 %
33330
33330
87.6 %
Total 5515
5515
100 %
26554
26554
100 %
5981
5981
100 %
38050
38050
100 %
χ2=107.839 · df=2 · Cramer’s V=0.053 · p=0.000
EDR employment match
emp.match Dem_Desc Total
Entirely rural Town/rural mix Urban/town/rural mix
Employment 676
819
12.3 %
4233
3944
15.9 %
742
888
12.4 %
5651
5651
14.9 %
No employment 4839
4696
87.7 %
22321
22610
84.1 %
5239
5093
87.6 %
32399
32399
85.1 %
Total 5515
5515
100 %
26554
26554
100 %
5981
5981
100 %
38050
38050
100 %
χ2=82.562 · df=2 · Cramer’s V=0.047 · p=0.000


Any three EDR employment match
emp.match Dem_Desc Total
Entirely rural Town/rural mix Urban/town/rural mix
Employment 726
893
13.2 %
4675
4301
17.6 %
762
969
12.7 %
6163
6163
16.2 %
No employment 4789
4622
86.8 %
21879
22253
82.4 %
5219
5012
87.3 %
31887
31887
83.8 %
Total 5515
5515
100 %
26554
26554
100 %
5981
5981
100 %
38050
38050
100 %
χ2=128.840 · df=2 · Cramer’s V=0.058 · p=0.000
MN employment match
emp.match Dem_Desc Total
Entirely rural Town/rural mix Urban/town/rural mix
Employment 841
1078
15.2 %
5552
5191
20.9 %
1046
1169
17.5 %
7439
7439
19.6 %
No employment 4674
4437
84.8 %
21002
21363
79.1 %
4935
4812
82.5 %
30611
30611
80.4 %
Total 5515
5515
100 %
26554
26554
100 %
5981
5981
100 %
38050
38050
100 %
χ2=112.162 · df=2 · Cramer’s V=0.054 · p=0.000


Factors - demographic

Race and Ethnicity

A significantly larger proportion of individuals that did not enroll in post-secondary within the first year of high school had meaningful employment the year they graduated high school.The percentage also increased for these individuals as the geography enlarged.


Race and ethnicity



County employment match
emp.match RaceEthnicity Total
AI Asian/PI Black Hispanic White
Employment 52
49
13.1 %
132
132
12.4 %
124
93
16.6 %
549
340
20 %
3860
4102
11.7 %
4717
4717
12.4 %
No employment 346
349
86.9 %
934
934
87.6 %
623
654
83.4 %
2194
2403
80 %
29196
28954
88.3 %
33293
33293
87.6 %
Total 398
398
100 %
1066
1066
100 %
747
747
100 %
2743
2743
100 %
33056
33056
100 %
38010
38010
100 %
χ2=174.487 · df=4 · Cramer’s V=0.068 · p=0.000
EDR employment match
emp.match RaceEthnicity Total
AI Asian/PI Black Hispanic White
Employment 55
59
13.8 %
168
158
15.8 %
134
111
17.9 %
623
408
22.7 %
4668
4912
14.1 %
5648
5648
14.9 %
No employment 343
339
86.2 %
898
908
84.2 %
613
636
82.1 %
2120
2335
77.3 %
28388
28144
85.9 %
32362
32362
85.1 %
Total 398
398
100 %
1066
1066
100 %
747
747
100 %
2743
2743
100 %
33056
33056
100 %
38010
38010
100 %
χ2=154.557 · df=4 · Cramer’s V=0.064 · p=0.000


Any three EDR employment match
emp.match RaceEthnicity Total
AI Asian/PI Black Hispanic White
Employment 69
65
17.3 %
173
173
16.2 %
148
121
19.8 %
665
445
24.2 %
5105
5357
15.4 %
6160
6160
16.2 %
No employment 329
333
82.7 %
893
893
83.8 %
599
626
80.2 %
2078
2298
75.8 %
27951
27699
84.6 %
31850
31850
83.8 %
Total 398
398
100 %
1066
1066
100 %
747
747
100 %
2743
2743
100 %
33056
33056
100 %
38010
38010
100 %
χ2=152.173 · df=4 · Cramer’s V=0.063 · p=0.000
MN employment match
emp.match RaceEthnicity Total
AI Asian/PI Black Hispanic White
Employment 79
78
19.8 %
224
209
21 %
186
146
24.9 %
762
537
27.8 %
6185
6467
18.7 %
7436
7436
19.6 %
No employment 319
320
80.2 %
842
857
79 %
561
601
75.1 %
1981
2206
72.2 %
26871
26589
81.3 %
30574
30574
80.4 %
Total 398
398
100 %
1066
1066
100 %
747
747
100 %
2743
2743
100 %
33056
33056
100 %
38010
38010
100 %
χ2=147.914 · df=4 · Cramer’s V=0.062 · p=0.000


The factors at Time X = grad year +2

The Classification and Regression Tree analysis concludes the following variables in our dataset as being important in predicting whether an individual has meaningful employment in the same regions as their high school the year of graduating high school.

  1. ps.grad.InstitutionSector
  2. avg.wages.pct.state
  3. cte.achievement
  4. ap.exam
  5. edr
  6. took.ACT
  7. RaceEthnicity
  8. pseo.participant
  9. Dem_Desc
  10. attended.ps.within.first.year.hsgrad

The following list includes these variables, as well as other variables (indicated with asterisk) that the cross-tab analysis indicated as having significant differences and helped explain variation.

  1. Post-secondary pathway
    1. ps.grad.InstitutionSector
    2. highest cred level*
    3. ps.grad*
    4. attended.ps*
  2. High school characteristics
    1. avg.wages.pct.mn
    2. EDR
    3. Dem_Desc
  3. High school accomplishments
    1. cte.achievement
    2. took.act
    3. ap.exam
  4. High school enrollment
    1. pseo.participant
    2. non-english home*
  5. Demographic variables
    1. Race/Ethnicity*

The charts below show the following;

A statistically significantly larger proportion of individuals with the following attributes had meaningful employment in the same geography as their high school;


Factors - post-secondary pathway

Post-secondary institution sector graduated

A significantly larger proportion of individuals that graduated from a 2-year public college had meaningful employment in the same geography as their high school two years after graduation. The percentage also increased for these individuals as the geography enlarged.

It’s also worth mentioning that individuals that did not graduate or attend post-secondary had a larger proportion of individuals with meaningful employment, as well as individuals who graduated from multiple institution sectors.


Post-secondary institution sector graduated



County employment match
emp.match ps.grad.InstitutionSector Total
Public, 2-year Did not graduate or
attend PS
Grad multiple
sectors
All 4-year Private and
for-profit 2-year or
less
Employment 1439
984
25.5 %
3488
2847
21.3 %
46
39
20.4 %
482
1243
6.8 %
82
423
3.4 %
5537
5537
17.4 %
No employment 4212
4667
74.5 %
12857
13498
78.7 %
179
186
79.6 %
6651
5890
93.2 %
2349
2008
96.6 %
26248
26248
82.6 %
Total 5651
5651
100 %
16345
16345
100 %
225
225
100 %
7133
7133
100 %
2431
2431
100 %
31785
31785
100 %
χ2=1327.407 · df=4 · Cramer’s V=0.204 · p=0.000
EDR employment match
emp.match ps.grad.InstitutionSector Total
Public, 2-year Did not graduate or
attend PS
Grad multiple
sectors
All 4-year Private and
for-profit 2-year or
less
Employment 1914
1270
33.9 %
4461
3674
27.3 %
59
51
26.2 %
619
1603
8.7 %
92
546
3.8 %
7145
7145
22.5 %
No employment 3737
4381
66.1 %
11884
12671
72.7 %
166
174
73.8 %
6514
5530
91.3 %
2339
1885
96.2 %
24640
24640
77.5 %
Total 5651
5651
100 %
16345
16345
100 %
225
225
100 %
7133
7133
100 %
2431
2431
100 %
31785
31785
100 %
χ2=1907.131 · df=4 · Cramer’s V=0.245 · p=0.000


Any three EDR employment match
emp.match ps.grad.InstitutionSector Total
Public, 2-year Did not graduate or
attend PS
Grad multiple
sectors
All 4-year Private and
for-profit 2-year or
less
Employment 2192
1434
38.8 %
5019
4148
30.7 %
62
57
27.6 %
700
1810
9.8 %
94
617
3.9 %
8067
8067
25.4 %
No employment 3459
4217
61.2 %
11326
12197
69.3 %
163
168
72.4 %
6433
5323
90.2 %
2337
1814
96.1 %
23718
23718
74.6 %
Total 5651
5651
100 %
16345
16345
100 %
225
225
100 %
7133
7133
100 %
2431
2431
100 %
31785
31785
100 %
χ2=2288.733 · df=4 · Cramer’s V=0.268 · p=0.000
MN employment match
emp.match ps.grad.InstitutionSector Total
Public, 2-year Did not graduate or
attend PS
Grad multiple
sectors
All 4-year Private and
for-profit 2-year or
less
Employment 2868
1965
50.8 %
6743
5682
41.3 %
105
78
46.7 %
1160
2480
16.3 %
174
845
7.2 %
11050
11050
34.8 %
No employment 2783
3686
49.2 %
9602
10663
58.7 %
120
147
53.3 %
5973
4653
83.7 %
2257
1586
92.8 %
20735
20735
65.2 %
Total 5651
5651
100 %
16345
16345
100 %
225
225
100 %
7133
7133
100 %
2431
2431
100 %
31785
31785
100 %
χ2=2848.137 · df=4 · Cramer’s V=0.299 · p=0.000